MultiQC Report

A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

This report has been generated by the genomic-medicine-sweden/nallo analysis pipeline.

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Report generated on 2025-03-31, 08:40 UTC based on data in: /scratch/nallo/416479/work/f8/831685a6f4d0e807bcf7763cba46b4

General Statistics

Showing ¹/₁ rows and ¹²/₂₅ columns.

Sample Name	≥ 1X	≥ 5X	≥ 10X	≥ 30X	≥ 50X	Median	Mean Cov.	Min Cov.	Max Cov.	Mb Total Coverage Bases	Genome length	Vars	SNP	Indel	Ts/Tv	MNP	% Dups	% GC	Average Read Length	Median Read Length	% Failed	M Seqs	% Phased Variants	Avg bp per Block	NG50
HG002	95.0%	94.0%	94.0%	87.0%	28.0%	45.0X	44.1	0.0	105065.0	136813.2Mb	3099812267	5521617	4286738	1234877	1.85	0	1.2%	40%	26164bp	24999bp	30%	7.9M	71%	3091491	29721333

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: table-general_stats_table

Sort	Group	Column	Description	ID	Scale
\|\|	mosdepth	≥ 1X	Fraction of genome with at least 1X coverage	`1_x_pc`	None
\|\|	mosdepth	≥ 5X	Fraction of genome with at least 5X coverage	`5_x_pc`	None
\|\|	mosdepth	≥ 10X	Fraction of genome with at least 10X coverage	`10_x_pc`	None
\|\|	mosdepth	≥ 30X	Fraction of genome with at least 30X coverage	`30_x_pc`	None
\|\|	mosdepth	≥ 50X	Fraction of genome with at least 50X coverage	`50_x_pc`	None
\|\|	mosdepth	Median	Median coverage	`median_coverage`	None
\|\|	mosdepth	Mean Cov.	Mean coverage	`mean_coverage`	None
\|\|	mosdepth	Min Cov.	Minimum coverage	`min_coverage`	None
\|\|	mosdepth	Max Cov.	Maximum coverage	`max_coverage`	None
\|\|	mosdepth	Mb Total Coverage Bases	Total coverage of bases (millions)	`coverage_bases`	base_count
\|\|	mosdepth	Genome length	Total length of the genome	`length`	None
\|\|	Bcftools: Stats	Vars	Variations total	`number_of_records`	None
\|\|	Bcftools: Stats	SNP	Variation SNPs	`number_of_SNPs`	None
\|\|	Bcftools: Stats	Indel	Variation Insertions/Deletions	`number_of_indels`	None
\|\|	Bcftools: Stats	Ts/Tv	Variant SNP transition / transversion ratio	`tstv`	None
\|\|	Bcftools: Stats	MNP	Variation multinucleotide polymorphisms	`number_of_MNPs`	None
\|\|	FastQC	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC	Average Read Length	Average Read Length (bp)	`avg_sequence_length`	None
\|\|	FastQC	Median Read Length	Median Read Length (bp)	`median_sequence_length`	None
\|\|	FastQC	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC	M Seqs	Total Sequences (millions)	`total_sequences`	read_count
\|\|	WhatsHap	% Phased Variants	Fraction of heterozygous variants that could be phased.	`frac_het_phased`	None
\|\|	WhatsHap	Avg bp per Block	Description of the distribution of non-singleton block lengths, where the length of a block is the number of base pairs it covers minus 1. That is, a block with two variants at positions 2 and 5 has length 3.	`bp_per_block_avg`	None
\|\|	WhatsHap	NG50	The NG50 value of the distribution of the block lengths. Interleaved blocks are cut in order to avoid artificially inflating this value.	`block_n50`	None

Somalier

Somalier calculates genotype :: pedigree correspondence checks from sketches derived from BAM/CRAM or VCF.DOI: 10.1186/s13073-020-00761-2.

Statistics

Various statistics from the somalier report.

Showing ¹/₁ rows and ⁹/₂₄ columns.

Sample Name	Phenotype	Sex	Father ID	Mother ID	Family ID	Inferred sex	HetVar	HomRefVar	HomAltVar	NA sites	Mean depth	Depth std	Sites depth	Genot depth std	Allele balance	Allele balance std	Allele balance < 0.2, > 0.8	HetVar X	HomRefVar X	HomAltVar X	Sites X	Mean depth X	Sites Y	Mean depth Y
HG002	2.0	male	0.0	0.0	HG002	1.0	6815	2946	3795	3828	44.7X	7.8	44.7X	7.8X	0.5	0.4	0.14	0	111	136	247	23.4X	16	20.6X

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: table-somalier_stats

Sort	Group	Column	Description	ID	Scale
\|\|	Somalier	Phenotype	Sample's phenotype from pedigree info	`phenotype`	None
\|\|	Somalier	Sex	Sample's sex from pedigree info	`original_pedigree_sex`	None
\|\|	Somalier	Father ID	ID of sample's father	`paternal_id`	None
\|\|	Somalier	Mother ID	ID of sample's mother	`maternal_id`	None
\|\|	Somalier	Family ID	ID of sample's family	`family_id`	None
\|\|	Somalier	Inferred sex	Sample's inferred sex	`sex`	None
\|\|	Somalier	HetVar	Heterozygous variants	`n_het`	variant_count
\|\|	Somalier	HomRefVar	Homozygous reference variants	`n_hom_ref`	variant_count
\|\|	Somalier	HomAltVar	Homozygous alternate variants	`n_hom_alt`	variant_count
\|\|	Somalier	NA sites	Unknown sites	`n_unknown`	None
\|\|	Somalier	Mean depth	Mean depth of all sites	`depth_mean`	None
\|\|	Somalier	Depth std	Depth's standard deviation of all sites	`depth_sd`	None
\|\|	Somalier	Sites depth	Mean depth of genotyped sites	`gt_depth_mean`	None
\|\|	Somalier	Genot depth std	Depth's standard deviation of genotype sites	`gt_depth_sd`	None
\|\|	Somalier	Allele balance	Mean allele balance	`ab_mean`	None
\|\|	Somalier	Allele balance std	Standard deviation of allele balance	`ab_std`	None
\|\|	Somalier	Allele balance < 0.2, > 0.8	Proportion of sites with allele balance < 0.2 or > 0.8	`p_middling_ab`	None
\|\|	Somalier	HetVar X	Heterozygous variants on X chromosome	`X_het`	variant_count_xy
\|\|	Somalier	HomRefVar X	Homozygous reference variants on X chromosome	`X_hom_ref`	variant_count_xy
\|\|	Somalier	HomAltVar X	Homozygous alternate variants on X chromosome	`X_hom_alt`	variant_count_xy
\|\|	Somalier	Sites X	Total sites on X chromosome	`X_n`	variant_count_xy
\|\|	Somalier	Mean depth X	Mean depth of sites on X chromosome	`X_depth_mean`	None
\|\|	Somalier	Sites Y	Total sites on Y chromosome	`Y_n`	variant_count_xy
\|\|	Somalier	Mean depth Y	Mean depth of sites on Y chromosome	`Y_depth_mean`	None

Heterozygosity

Standard deviation of heterozygous allele balance against mean depth.

A high standard deviation in allele balance suggests contamination.

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Sex

Predicted sex against scaled depth on X

Higher values of depth, low values suggest male.

Created with MultiQC

mosdepth

mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.

Cumulative coverage distribution

Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 1 samples, a BED file was provided, so the data was calculated across those regions. For 1 samples, it's calculated across the entire genome length. 1 samples have both global and region reports, and we are showing the data for regions

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

Created with MultiQC

Coverage distribution

Proportion of bases in the reference genome with a given depth of coverage. Note that for 1 samples, a BED file was provided, so the data was calculated across those regions. For 1 samples, it's calculated across the entire genome length. 1 samples have both global and region reports, and we are showing the data for regions

Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

Created with MultiQC

Average coverage per contig

Average coverage per contig or chromosome

Created with MultiQC

XY coverage

Created with MultiQC

Bcftools

Bcftools contains utilities for variant calling and manipulating VCFs and BCFs.DOI: 10.1093/gigascience/giab008.

Variant Substitution Types

Created with MultiQC

Variant Quality

Created with MultiQC

Indel Distribution

Created with MultiQC

FastQC

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

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Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

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Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

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Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

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Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

1 samples had less than 1% of reads made up of overrepresented sequences

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ¹/₁ rows and ³/₃ columns.

Overrepresented sequence	Samples	Occurrences	% of all reads
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	1	69433	0.8776%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: table-fastqc_top_overrepresented_sequences_table

Sort	Group	Column	Description	ID	Scale
\|\|	FastQC	Samples	Number of samples where this sequence is overrepresented	`samples`	None
\|\|	FastQC	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`total_count`	None
\|\|	FastQC	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`total_percent`	None

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

WhatsHap

WhatsHap is a program for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but also works well with short reads. .DOI: 10.1101/085050.

Phased Basepairs per Sample

This plot show the total number of phased base pairs for each sample.

Created with MultiQC

WhatsHap statistics

Showing ¹/₁ rows and ¹⁰/₁₃ columns.

Sample Name	Input Variants	Heterozygous Variants	Heterozygous SNVs	Unphased Variants	Phased Variants	Phased SNVs	Blocks	Singletons	Total Phased bp	Avg Variants per Block	% Phased Variants	Avg bp per Block	NG50
HG002	12823444	4590266	3223770	1349119	3241145	2425205	871	2	2701963137	3721.2	71%	3091491	29721333

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: table-whatshap-stats-table

Sort	Group	Column	Description	ID	Scale
\|\|	WhatsHap	Input Variants	Number of biallelic variants in the input VCF, but excluding any non-SNV variants if --only-snvs was used	`variants`	None
\|\|	WhatsHap	Heterozygous Variants	The number of biallelic, heterozygous variants in the input VCF. This is a subset of Input Variants.	`heterozygous_variants`	None
\|\|	WhatsHap	Heterozygous SNVs	The number of biallelic, heterozygous SNVs in the input VCF. This is a subset of Heterozygous Variants.	`heterozygous_snvs`	None
\|\|	WhatsHap	Unphased Variants	The number of biallelic, heterozygous variants that are not marked as phased in the input VCF. This is a subset of heterozygous_variants.	`unphased`	None
\|\|	WhatsHap	Phased Variants	The number of biallelic, heterozygous variants that are marked as phased in the input VCF. This is a subset of heterozygous_variants. Also, phased + unphased + singletons = heterozygous_variants.	`phased`	None
\|\|	WhatsHap	Phased SNVs	The number of biallelic, heterozygous SNVs that are marked as phased in the input VCF. This is a subset of phased.	`phased_snvs`	None
\|\|	WhatsHap	Blocks	The total number of phase sets/blocks.	`blocks`	None
\|\|	WhatsHap	Singletons	The number of blocks that contain exactly one variant.	`singletons`	None
\|\|	WhatsHap	Total Phased bp	The sum of the lengths of all non-singleton blocks, where the length of a block is the number of base pairs it covers minus 1. That is, a block with two variants at positions 2 and 5 has length 3.	`bp_per_block_sum`	None
\|\|	WhatsHap	Avg Variants per Block	Description of the distribution of non-singleton block sizes, where the size of a block is the number of variants it contains. Number of biallelic variants in the input VCF, but excluding any non-SNV variants if --only-snvs was used.	`variant_per_block_avg`	None
\|\|	WhatsHap	% Phased Variants	Fraction of heterozygous variants that could be phased.	`frac_het_phased`	None
\|\|	WhatsHap	Avg bp per Block	Description of the distribution of non-singleton block lengths, where the length of a block is the number of base pairs it covers minus 1. That is, a block with two variants at positions 2 and 5 has length 3.	`bp_per_block_avg`	None
\|\|	WhatsHap	NG50	The NG50 value of the distribution of the block lengths. Interleaved blocks are cut in order to avoid artificially inflating this value.	`block_n50`	None

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
BCFTOOLS_CONCAT	bcftools	`1.2`
BCFTOOLS_FILLTAGS	bcftools	`OpenBLAS`
BCFTOOLS_INDEX_MERGE	bcftools	`1.2`
BCFTOOLS_MERGE	bcftools	`1.2`
BCFTOOLS_NORM_MULTISAMPLE	bcftools	`1.2`
BCFTOOLS_NORM_SINGLESAMPLE	bcftools	`1.2`
BCFTOOLS_PLUGINSPLIT	bcftools	`OpenBLAS`
BCFTOOLS_SORT_TRGT	bcftools	`1.2`
BCFTOOLS_STATS	bcftools	`OpenBLAS`
BEDTOOLS_MERGE	bedtools	`2.31.1`
BEDTOOLS_SORT	bedtools	`2.31.1`
BUILD_INTERVALS	gawk	`5.1.0`
COMPRESS_STRANGER	bcftools	`1.2`
CRAMINO	cramino	`0.14.5`
CRAMINO_PHASED	cramino	`0.14.5`
DEEPVARIANT	deepvariant	`1.6.1`
ECHTVAR_ENCODE	echtvar	`0.2.0`
FQCRS	fqcrs	`0.1.0`
FastQC	fastqc	`0.12.1`
GLNEXUS	glnexus	`1.4.3-0-gcecf42e`
HIFICNV	hificnv	`0.1.7-70e9988`
MINIMAP2_ALIGN	minimap2	`2.28.post1209`
	samtools	`1.2`
MINIMAP2_INDEX	minimap2	`2.28.post1209`
PARAPHASE	minimap2	`2.28.post1209`
	paraphase	`3.1.1`
	samtools	`1.2`
RELATE_RELATE	somalier	`0.2.18`
SAMPLESHEET_PED	create_pedigree_file	`1.0`
	python	`3.8.3`
SAMTOOLS_FAIDX	samtools	`1.2`
SAMTOOLS_FASTQ	samtools	`1.2`
SAMTOOLS_INDEX_TRGT	samtools	`1.2`
SAMTOOLS_INDEX_WHATSHAP	samtools	`1.2`
SAMTOOLS_SORT_TRGT	samtools	`1.2`
SNIFFLES	sniffles	`2.0.7`
SNIFFLES_MULTISAMPLE	sniffles	`2.0.7`
SOMALIER_EXTRACT	somalier	`0.2.18`
SOMALIER_PED	create_pedigree_file	`1.0`
	python	`3.8.3`
SPLIT_BED_CHUNKS	split_bed_chunks	`1.0`
STRANGER	stranger	`0.9.1`
TABIX_BGZIPTABIX	tabix	`1.2`
TABIX_HIFICNV	tabix	`1.2`
TABIX_TABIX	tabix	`1.2`
TRGT	trgt	`trgt 0.7.0-4941fbb`
WHATSHAP_HAPLOTAG	whatshap	`2.2`
WHATSHAP_PHASE	bgzip	`1.2`
	tabix	`1.2`
	whatshap	`2.2`
WHATSHAP_STATS	whatshap	`2.2`
Workflow	Nextflow	`24.10.3`
	genomic-medicine-sweden/nallo	`v0.3.1-gf0abc01`
mosdepth	mosdepth	`0.3.8`

genomic-medicine-sweden/nallo Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.

Methods

Data was processed using genomic-medicine-sweden/nallo v0.3.1 which uses the nf-core template (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v24.10.3 (Di Tommaso et al., 2017) with the following command:

nextflow run genomic-medicine-sweden/nallo -r 0.3.1 -resume -profile docker -process.executor=slurm -process.queue=high --input /persistent/development/600_wgs_ont_human_rare_disease/input/samplesheet.csv --fasta /persistent/development/reference/GCA_000001405.15_GRCh38_no_alt_analysis_set/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna --somalier_sites /persistent/development/600_wgs_ont_human_rare_disease/input/sites.hg38.vcf.gz --par_regions /persistent/development/600_wgs_ont_human_rare_disease/input/GRCh38_PAR.bed --hificnv_xy /persistent/development/600_wgs_ont_human_rare_disease/input/expected_cn.hg38.XY.bed --hificnv_xx /persistent/development/600_wgs_ont_human_rare_disease/input/expected_cn.hg38.XX.bed --hificnv_exclude /persistent/development/600_wgs_ont_human_rare_disease/input/cnv.excluded_regions.hg38.bed.gz --trgt_repeats /persistent/development/600_wgs_ont_human_rare_disease/input/pathogenic_repeats.hg38.bed --variant_catalog /persistent/development/600_wgs_ont_human_rare_disease/input/variant_catalog_grch38.json --vep_cache /persistent/development/600_wgs_ont_human_rare_disease/input/vep_cache --vep_plugin_files /persistent/development/600_wgs_ont_human_rare_disease/input/vep_plugins.csv --snp_db /persistent/development/600_wgs_ont_human_rare_disease/input/snp_db.csv --variant_consequences_snv /persistent/development/600_wgs_ont_human_rare_disease/input/variant_consequences_v2.txt --score_config_snv /persistent/development/600_wgs_ont_human_rare_disease/input/rank_model_snv.ini --reduced_penetrance /persistent/development/600_wgs_ont_human_rare_disease/input/reduced_penetrance.tsv --outdir /persistent/development/600_wgs_ont_human_rare_disease/output_0.3.1_no_anno/test_out --skip_assembly_wf --skip_methylation_wf --skip_snv_annotation --skip_rank_variants --skip_repeat_calling false

Tools used in the workflow included: MultiQC (Ewels et al. 2016), SAMtools (Danecek et al. 2021), FastQC (Andrews 2010), fcqrs, Minimap2 (Li 2018), Somalier (Pedersen et al. 2020), Sniffles2 (Smolka et al. 2024), cramino (De Coster & Rademakers 2023), mosdepth (Pedersen & Quinlan 2018), paraphase, BEDTools (Quinlan & Hall 2010), BCFtools (Danecek et al. 2021), DeepVariant (Poplin et al. 2018), GLnexus (Yun et al. 2021), HiFiCNV, WhatsHap (Martin et al. 2016), TRGT (Dolzhenko et al. 2024), Stranger (Nilsson & Magnusson 2021).

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
Andrews S, (2010) FastQC, URL: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.
Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.
Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.
Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10(2):giab008. doi:10.1093/gigascience/giab008
Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26, 6, pp. 841–842.
Wouter De Coster, Rosa Rademakers, NanoPack2: population-scale evaluation of long-read sequencing data, Bioinformatics, Volume 39, Issue 5, May 2023, btad311, https://doi.org/10.1093/bioinformatics/btad311
Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13(1):31. doi:10.1186/s13073-021-00835-9
Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Research. 2019;47(D1):D886-D894. doi:10.1093/nar/gky1016
Poplin R, Chang PC, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-987. doi:10.1038/nbt.4235
Li H, Bloom JM, Farjoun Y, Fleharty M, Gauthier L, Neale B, MacArthur D (2018) A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat Methods, 15:595-597. [PMID:30013044]
Brent S Pedersen, Jeroen de Ridder, Echtvar: compressed variant representation for rapid annotation and filtering of SNPs and indels, Nucleic Acids Research, Volume 51, Issue 1, 11 January 2023, Page e3, https://doi.org/10.1093/nar/gkac931
McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1):122. doi:10.1186/s13059-016-0974-4
Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].
Magnusson M, Hughes T, Glabilloy, Bitdeli Chef. genmod: Version 3.7.3. Published online November 15, 2018. doi:10.5281/ZENODO.3841142
Giulio Formenti, Linelle Abueg, Angelo Brajuka, Nadolina Brajuka, Cristóbal Gallardo-Alba, Alice Giani, Olivier Fedrigo, Erich D Jarvis, Gfastats: conversion, evaluation and manipulation of genome sequences using assembly graphs, Bioinformatics, Volume 38, Issue 17, September 2022, Pages 4214–4216, https://doi.org/10.1093/bioinformatics/btac460
Yun T, Li H, Chang PC, Lin MF, Carroll A, McLean CY. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Robinson P, ed. Bioinformatics. 2021;36(24):5582-5589. doi:10.1093/bioinformatics/btaa1081
Cheng, H., Concepcion, G.T., Feng, X. et al. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021). https://doi.org/10.1038/s41592-020-01056-5
James M Holt, Christopher T Saunders, William J Rowell, Zev Kronenberg, Aaron M Wenger, Michael Eberle, HiPhase: jointly phasing small, structural, and tandem repeat variants from HiFi sequencing, Bioinformatics, Volume 40, Issue 2, February 2024, btae042, https://doi.org/10.1093/bioinformatics/btae042
Heng Li, Minimap2: pairwise alignment for nucleotide sequences, Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094–3100, https://doi.org/10.1093/bioinformatics/bty191
Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Hancock J, ed. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699
Genome-wide profiling of highly similar paralogous genes using HiFi sequencing. Xiao Chen, Daniel Baker, Egor Dolzhenko, Joseph M Devaney, Jessica Noya, April S Berlyoung, Rhonda Brandon, Kathleen S Hruska, Lucas Lochovsky, Paul Kruszka, Scott Newman, Emily Farrow, Isabelle Thiffault, Tomi Pastinen, Dalia Kasperaviciute, Christian Gilissen, Lisenka Vissers, Alexander Hoischen, Seth Berger, Eric Vilain, Emmanuèle Délot, UCI Genomics Research to Elucidate the Genetics of Rare Diseases (UCI GREGoR) Consortium, Michael A Eberle. bioRxiv 2024.04.19.590294; doi: https://doi.org/10.1101/2024.04.19.590294
Smolka, M., Paulin, L.F., Grochowski, C.M. et al. Detection of mosaic and population-level structural variants with Sniffles2. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02024-y
Pedersen, B.S., Bhetariya, P.J., Brown, J. et al. Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches. Genome Med 12, 62 (2020). https://doi.org/10.1186/s13073-020-00761-2
Nilsson D, Magnusson M. moonso/stranger v0.7.1. Published online February 18, 2021. doi:10.5281/ZENODO.4548873
Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27(5):718-719. doi:10.1093/bioinformatics/btq671
Dolzhenko, E., English, A., Dashnow, H. et al. Characterization and visualization of tandem repeats at genome scale. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02057-3
Marcel Martin, Murray Patterson, Shilpa Garg, Sarah O Fischer, Nadia Pisanti, Gunnar W Klau, Alexander Schöenhuth, Tobias Marschall. bioRxiv 085050; doi: https://doi.org/10.1101/085050
Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.
Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.
da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.
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Notes:

If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used.
The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

genomic-medicine-sweden/nallo Workflow Summary

- this information is collected when the pipeline is started.

Core Nextflow options

revision: 0.3.1
runName: gigantic_borg
containerEngine: docker
launchDir: /scratch/nallo/416479
workDir: /scratch/nallo/416479/work
projectDir: /scratch/nallo/416479/.nextflow/assets/genomic-medicine-sweden/nallo
userName: root
profile: docker
configFiles: N/A

Workflow skip options

skip_assembly_wf: true
skip_methylation_wf: true
skip_snv_annotation: true
skip_rank_variants: true

Input/output options

input: /persistent/development/600_wgs_ont_human_rare_disease/input/samplesheet.csv
outdir: /persistent/development/600_wgs_ont_human_rare_disease/output_0.3.1_no_anno/test_out

Reference genome options

fasta: /persistent/development/reference/GCA_000001405.15_GRCh38_no_alt_analysis_set/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna

Workflow options

vep_plugin_files: /persistent/development/600_wgs_ont_human_rare_disease/input/vep_plugins.csv

File inputs

par_regions: /persistent/development/600_wgs_ont_human_rare_disease/input/GRCh38_PAR.bed
trgt_repeats: /persistent/development/600_wgs_ont_human_rare_disease/input/pathogenic_repeats.hg38.bed
snp_db: /persistent/development/600_wgs_ont_human_rare_disease/input/snp_db.csv
variant_catalog: /persistent/development/600_wgs_ont_human_rare_disease/input/variant_catalog_grch38.json
variant_consequences_snv: /persistent/development/600_wgs_ont_human_rare_disease/input/variant_consequences_v2.txt
vep_cache: /persistent/development/600_wgs_ont_human_rare_disease/input/vep_cache
hificnv_xy: /persistent/development/600_wgs_ont_human_rare_disease/input/expected_cn.hg38.XY.bed
hificnv_xx: /persistent/development/600_wgs_ont_human_rare_disease/input/expected_cn.hg38.XX.bed
hificnv_exclude: /persistent/development/600_wgs_ont_human_rare_disease/input/cnv.excluded_regions.hg38.bed.gz
reduced_penetrance: /persistent/development/600_wgs_ont_human_rare_disease/input/reduced_penetrance.tsv
score_config_snv: /persistent/development/600_wgs_ont_human_rare_disease/input/rank_model_snv.ini
somalier_sites: /persistent/development/600_wgs_ont_human_rare_disease/input/sites.hg38.vcf.gz

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